Methods and systems for generating recommendation list with diversity

Abstract

Systems and methods for automatically generating a user-specific recommendation list with diversified as well as relevant items. Recommendation categories and sample items for each category are diversified by virtue of probability distribution. Items in a user' collection can be clustered based on category in accordance with a similarity measure. The category probability distribution values may be derived from characteristics suggesting respective categories' importance or preference to the user. For each selected category, the sample probability distribution values may be derived from similarities of each candidate to the selected category.

Description

TECHNICAL FIELD[0001]The present disclosure relates generally to the field of e-commerce marketing, and, more specifically, to the field of automatic generation of recommendation items.BACKGROUND[0002]Presenting customized recommendation lists of relevant products based on individual consumers' shopping behavior patterns has become increasingly important for e-commerce companies in order for them to effectively attract and retain consumers. Many of the recommendation systems have adopted Collaborative Filtering (CF) approaches in which recommendations are made based on a user's manifested preferences, e.g., satisfactory ratings, on particular products.[0003]A typical CF recommendation generation process in accordance with the prior art primarily comprises steps as follows. Let U be the set of all users, and I be the set of all items. Let Iu be the set of items that have been purchased or rated or commented on by user u ∈ U. For example, Iu may correspond to a user's library or colle...

AI Technical Summary

Benefits of technology

The present invention provides a computer-implemented method and system for automatically generating recommendation lists for users based on their past behavior. The system accesses a user's inventory and a stock of commodities or books and assigns probability distribution values to each category or topic. The system then selects a set of recommendations for each category or topic based on the probability distribution values and the similarity between the recommendations and the user's collection. The resulting recommendation lists include a diversified mix of relevant items that meet the user's preferences. The technical effects of this invention include improved user experience, improved recommendations, and improved diversification of recommendations.

Problems solved by technology

Therefore, although these systems can make recommendations on products closely relevant to user's prior individual purchases, the recommendation lists lack ˜diversity as well as other global measures that may be useful to influence a user's interest of purchasing.

As an unfortunate result, a user likely receives very similar or even the same list repeatedly and the distribution may become stale.

Existing efforts directed to introduce diversification to recommendations are quite ad-hoc at a cost of recommendation performance.

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